A survey on efficient no reference blur estimation methods

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 201
A SURVEY ON EFFICIENT NO-REFERENCE BLUR ESTIMATION
METHODS
Deepa Maria Thomas1
, S. John Livingston2
1
PG Scholar, 2
Asst. Professor, School of Computer Science, Karunya University, Tamil Nadu, India,
deepamariathomas@gmail.com, johnlivingston@karunya.edu
Abstract
Blur estimation in image processing has come to be of great importance in assessing the quality of images. This work presents a
survey of no-reference blur estimation methods. A no-reference method is particularly useful, when the input image used for blur
estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four
no-reference blur estimation methods. The first method applies a scale adaptive technique of blur estimation to get better accuracy in
the results. The second blur metric involves finding the energy using second order derivatives of an image using derivative pair of
quadrature filters. The third blur metric is based on the kurtosis measurement in the discrete dyadic wavelet transform (DDWT) of the
images. The fourth method of blur estimation is obtained by finding the ratio of sum of the edge widths of all the detected edges to the
total number of edges. The results provided are useful in comparing the methods based on metrics like Spearman correlation
coefficient. The results are obtained by evaluation on images from the Laboratory for Image and Video Engineering (LIVE) database.
The various methods are evaluated on the images by adding varying content of noise. The performance is evaluated for 3 different
categories namely Gaussian blur, motion blur and also JPEG2000 compressed images. Blur estimation finds its application in quality
assessment, image fusion and auto-focusing in images. The sharpness of an image can also be found from the blur metric as sharpness
is inversely proportional to blur. Sharpness metrics can also be combined with other metrics to measure overall quality of the image.
Keywords: Blur estimation, blur, no-reference
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1. INTRODUCTION
What we see has a profound effect on what we do, how we
feel, and who we are. It is through experience and a lot of
experimentation that we continually increase our
understanding of the visual world. Psychologists found that
93% of our daily communication is nonverbal. Studies have
also found that the human brain deciphers image elements
simultaneously [1]. To perceive all these features correctly in
an image, it should be free from irregularities like blur. This is
where blur estimations comes to be of great importance.
Blur estimation method can be broadly divided into three
categories: full-reference, reduced-reference and no-reference.
In full-reference methods the processed image is compared with
a reference image. On the other hand, in a no-reference method,
only the input image is processed. This method is particularly
useful, when the input image used for blur estimation does not
have an available corresponding reference image. With reduced
reference methods, there is no reference image, but some of the
features of the reference image are known. In this paper, we
compare some no-reference blur estimation methodologies and
their performance on images distorted with different types of
blur. The input images were taken for evaluation from the
Laboratory of Image and Video (LIVE) database [2] available
on the net.
2. METHODOLOGIES OF NO-REFERENCE
BLUR ESTIMATION METHODS
2.1 Scale Adaptive Method Based on Wavelet
Transform (SPADU)
This method was proposed by Ducottet [3] and is based on
wavelet transform to perform edge detection. The exclusive
feature of this method is that it performs modeling the
contours in an image into three types of singularities namely,
line, transition and peak.
The transition edge corresponds to the boundaries of the
image. The peak model is modeled as the convolution product
of a two dimensional Dirac function and a Gaussian function.
The line model is taken as the convolution product of a one
dimensional Dirac function with a Gaussian function.The
methodology can be described as below.
Step1:
Wavelet local maxima is extracted across scales for each type
of singularity-transition, line and peak. Maxima function is
obtained by plotting wavelet maxima function across y axis
and scale across the x axis.

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Step2:
Wavelet maxima function that is extracted from Step1 is
compared with the theoretical maxima function. The local
edge type is determined by finding which extracted maxima
function model is the closest. This is done by performing
fitting.
Step3:
The fitting that gives the highest correlation coefficient
provides the type of corresponding edge points and parameters
like amplitude and smoothing size.
The flowchart is shown in Fig -1.
Fig -1: Flowchart of scale adaptive blur estimation.
2.2 Blur Estimation Using Energy Of Quadrature
Filters (SPAQF)
This blur estimation method [4] has considerable advantage
over the scale adaptive wavelet based method described
earlier. The limitation of a large number of processing scales is
removed because only one response is generated for a
particular scale. So this reduces the computational complexity.
The quadrature filters used in this method are derivative pairs
which has causality property. The flowchart for this method is
shown in Fig -2. The steps are as follows:
Step1:
The first order and second order derivatives of the image are
calculated.
Step2:
From the derivatives, the energy can be calculated which is
given by Eq. 1:
2''2'
)),,,(()),,,(),,,(  syxfsyxfsyxE  (1)
Step3:
The direction of maximum energy is found and the energy is
saved.
Step4:
The extrema function is constructed by finding the
corresponding extrema of energy across scales.
Step5:
The extrema function of the models is fitted with the extracted
extrema function to estimate the blur level.
Step6:
Calculate average of the blurriness of all the edges detected.
Fig -2: Flowchart of quadrature filters method
2.3 Kurtosis Method of Blur Estimation
The main ideology behind this blur metric [5] is based on
Lipschitz regularities properties. These properties are used to
separate the singularities of the image from noise.The
Lipschitz property indicates that noise isreducedas the scale
increases in wavelet domain [6]. This method produces a

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sharpness metric. Since sharpness is inversely proportional to
blur, by taking the inverse of the sharpness metric, the blur
metric can be deduced. A smaller kurtosis indicates a sharper
image. The kurtosis measureis appied on the detailed subbands
(both horizontal and vertical) obtained from the DDWT of the
image. The discrete dyadic wavelet transform has the
advantage of providing stability as well as ease of
implementation. The flowchart for this method is shown in Fig
-3. The steps of this method are as below:
Step1:
Perform Discrete dyadic wavelet transform (DDWT) in two
dimension (2D) This produced two detailed sub band images.
Step2:
Three level wavelet decomposition is performed on the image.
Step3:
The noise and edge singularities are separated using Lipschitz
coefficients. The Lipschitz coefficients are measured from the
evolution of wavelet maxima across scales. If the Lipschitz
regularity is positive, the amplitude of the wavelet transform
modulus maxima should decrease as scale decreases. This is
the case for discontinuities. On the contrary, for singularities
the modulus maxima increases as scale decreases.
Fig -3: Flowchart of kurtosis method of blur estimation.
2.4 Marziliano Method of Blur Estimation
(Marziliano)
This is a perceptual method [7] of blur estimation. The images
are considered for evaluation by expert viewers. The viewers
are asked to give ratings and the Mean Objective Score (MOS)
is considered for evaluation. This method provides an image
quality evaluation technique as well. The advantage of the
Marziliano metric is its low complexity. This enables the use
of this metric in real time applications [8,9]. The flowchart is
shown in Fig -4.The steps are as follows:
Step1:
Perform edge detection using Sobel filter.
Step2:
Using thresholding, noise and other insignificant edges are
removed.
Step3:
Find the starting and ending position for each and every row
of the processed image.
Step4:
Calculate local blur value as the difference between start and
end positions.
Step5:
For all the edges, calculate sum of all edge widths.
Step6:
Calculate blur measure
Fig -4: Flowchart of the Marziliano method

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3. COMPARISON OF THE METHODS
Kurtosis based method of blur estimation only works well
under low to medium signal to noise ratio (SNR). Ducottet
method on the other hand provides a better performance in the
presence of a larger SNR. However, for Ducottet method,
multiple responses are produced. By multiple responses what
is implied is that more than one local maxima is produced. The
presence of multiple responses increases the computational
cost. However, in the quadrature filter method of blur
estimation, the number of processing scales is significantly
reduced. This diminishes the computational cost considerably.
The advantage of the Marziliano metric is its low complexity.
This enables the use of this metric in real time applications.
We find that the blur estimation method based on the energy of
quadrature filters does not perform as well as the other
methods for JPEG2000 compressed images. However, as the
noise increases, the performance improves. This is a clear
distinction from the other methods described because the
performance generally decreases as the noise content increase
in the images.
4. EXPERIMENTAL RESULTS
The images with different types of blur were evaluated with
the above blur estimation methods. The images were distorted
with different levels of gaussian and motion blur. The methods
were also evaluated for JPEG2000 compressed images with
varying STD of noise.
LIVE database also provides a document containing the actual
amount of blur introduced in the test images. The metric
considered for performance evaluation is Spearman
correlation coefficient. The Spearman correlation coefficient is
used for comparing the extent of the relationship between two
sets of data. It can take values between -1 and 1. A value of 1
indicates the best correlation and -1 indicated the worst.
Each of the blurred images are evaluated by introducing noise
of STD’s 0, 5 and 15. Table 1 denotes Spearman correlation
coefficient of the images for Gaussian blurred images, Table 2
denotes the correlation coefficient for motion blurred images
and Table 3 denotes the correlation findings for JPEG2000
compressed images.
The results that are shown below are obtained from the
evaluation results of [3].The notations used in the tables are
explained as below
 SPADU_6 refers to Ducottet method with 6 scales in
spatial domain
 SPADU_8 refers to Ducottet method with 8 scales in
spatial domain
 FFTDU_6 refers to Ducottet method with 6 scales in
frequency domain
 FFTDU_8 refers to Ducottet method with 8 scales in
frequency domain
 SPAQF refers to Quadrature filter method in spatial
domain
 FFTQF refers to Quadrature filter method in
frequency domain
 Marziliano refers to the perceptual blur estimation
method
Table -1: Spearman correlation coefficient for Gaussian
blurred images
Noise STD=0 Noise STD=5 Noise STD=10
SPAQ
F
1.00000 SPAQ
F
0.99979 SPAQ
F
0.99900
SPAD
U_8
0.99986 FFTQ
F
0.99929 FFTQ
F
0.99801
FFTQ
F
0.99986 SPAD
U_8
0.99658 SPAD
U_8
0.99395
SPAD
U_6
0.99957 FFTD
U_8
0.99488 FFTD
U_8
0.99388
FFTD
U_8
0.99957 SPAD
U_6
0.99388 SPAD
U_6
0.98932
FFTD
U_6
0.99936 FFTD
U_6
0.98763 Marzili
ano
0.98484
Marzili
ano
0.82634 Marzili
ano
0.97153 FFTD
U_6
0.97787
Table -2: Spearman correlation coefficient for motion blurred
images
SPAQ
F
0.980 SPAQ
F
0.490 SPAQ
F
0.330
Marzili
ano
0.550 Marzili
ano
0.070 Marzili
ano
0.210
Table -3: Spearman correlation coefficient for JPEG2000
images
Marzili
ano
0.998 Marzili
ano
0.985 Marzili
ano
0.949
SPAQ
F
0.777 SPAQ
F
0.819 SPAQ
F
0.811
CONCLUSIONS
This work provides the comparative study of some of the no-
reference methods of blur estimation. The evaluation was done
on images distorted with Gaussian blur and motion blur from
LIVE database. It was found that the quadrature filter approach
to blur estimation provided very good performance, except in
case of JPEG2000 compressed images. For JPEG2000

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compressed images, the correlation coefficient is lower than
other popular blur estimation methods. This is probably due to
the fact that compressing introduced other features like ringing
in the compressed images.
REFERENCES
[1] A. Mehrabian, “Nonverbal communication,” Library of
Congress, 2009, United States of America 1992.
[2] H.R. Sheikh, Z. Wang, L. Cornack, A.C. Bovik, Live
Image Quality Assessment Database Release2.
<http://live.ece.utexas.edu/research/quality>.
[3] C. Ducottet, T. Fournel, C.Barat, “Scale-adaptive
detection and local characterization of edges based on
wavelet transform,” Signal Processing 84(11), pp.
2115-2137, 2004.
[4] S. Soleimani, F. Rooms, W. Philips, “Efficient blur
estimation using multi scale quadrature filters,” Signal
Processing 93, pp. 1988-2002, 2013.
[5] R. Ferzli, L.J. Karam, J. Caviedes, “A robust image
sharpness metric based on kurtosis measurement of
wavelet coefficients,” in: International Workshop on
Video Processing and Quality Metrics for Consumer
Electronics, 2005.
[6] S. Mallat and W.L. Hwang, “Singularity detection and
processing using wavelets,” IEEE Trans. on
Information theory, Vol 38, pp. 617-643, 1992.
[7] P. Marziliano, F. Dufaux, S. Winkler, T. Ebrahimi,
“Perceptual blur and ringing metrics: application to
JPEG2000,” Signal Processing :Image Communication
19(2), pp. 163-172, 2004.
[8] R.L. Lagendijk, J. Biemond, “Basic methods of image
restoration and identification,” Handbook of Image and
Video Processing, Ch 3.5, pp.125-139, 2000.
[9] V. Kayargadde, J.B. Martens, “Perceptual
characterization of images degraded by blur and noise,”
Journal of Optical Society of America pp. 1178-1188,
1996.
BIOGRAPHIES:
Deepa Maria Thomas received the B.E. degree
in Electronics and Communication Engineering
from Karunya University in 2010. During 2010-
2012, she worked for a Multinational company
providing IT services in Mainframe Technologies.
She is currently pursuing M.Tech in Computer Science and
Engineering at Karunya University.
S. John Livingston received his M.E. degree
from Karunya University and is currently working
as Asst. Professor in the School of Computer
Science.

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